Overview

Dataset statistics

Number of variables10
Number of observations1440
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory112.6 KiB
Average record size in memory80.1 B

Variable types

DateTime1
Numeric9

Warnings

time has unique values Unique
infusion_pressure has 307 (21.3%) zeros Zeros

Reproduction

Analysis started2021-04-25 17:44:44.337689
Analysis finished2021-04-25 17:44:56.544471
Duration12.21 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

time
Date

UNIQUE

Distinct1440
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size11.4 KiB
Minimum2021-04-23 16:00:36.351072
Maximum2021-04-24 15:59:36.924559
2021-04-26T01:44:56.693092image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T01:44:56.849677image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

ch1
Real number (ℝ≥0)

Distinct77
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.113454306
Minimum1.0257
Maximum1.1307
Zeros0
Zeros (%)0.0%
Memory size11.4 KiB
2021-04-26T01:44:57.004276image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1.0257
5-th percentile1.1037
Q11.1097
median1.1137
Q31.1177
95-th percentile1.1247
Maximum1.1307
Range0.105
Interquartile range (IQR)0.008

Descriptive statistics

Standard deviation0.00761866665
Coefficient of variation (CV)0.006842370281
Kurtosis22.88085136
Mean1.113454306
Median Absolute Deviation (MAD)0.004
Skewness-2.473525276
Sum1603.3742
Variance5.804408152 × 105
MonotocityNot monotonic
2021-04-26T01:44:57.156377image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.113785
 
5.9%
1.115783
 
5.8%
1.110777
 
5.3%
1.109775
 
5.2%
1.112775
 
5.2%
1.111771
 
4.9%
1.116770
 
4.9%
1.114767
 
4.7%
1.108766
 
4.6%
1.117765
 
4.5%
Other values (67)706
49.0%
ValueCountFrequency (%)
1.02571
0.1%
1.03171
0.1%
1.06871
0.1%
1.07271
0.1%
1.07671
0.1%
ValueCountFrequency (%)
1.13071
 
0.1%
1.12973
0.2%
1.12952
 
0.1%
1.12871
 
0.1%
1.12777
0.5%

ch2
Real number (ℝ≥0)

Distinct106
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7165904167
Minimum0.5897
Maximum0.7357
Zeros0
Zeros (%)0.0%
Memory size11.4 KiB
2021-04-26T01:44:57.308548image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0.5897
5-th percentile0.7037
Q10.7127
median0.7187
Q30.7247
95-th percentile0.7297
Maximum0.7357
Range0.146
Interquartile range (IQR)0.012

Descriptive statistics

Standard deviation0.01481324717
Coefficient of variation (CV)0.0206718466
Kurtosis26.00016755
Mean0.7165904167
Median Absolute Deviation (MAD)0.006
Skewness-4.364070019
Sum1031.8902
Variance0.0002194322917
MonotocityNot monotonic
2021-04-26T01:44:57.459176image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.724778
 
5.4%
0.725775
 
5.2%
0.723765
 
4.5%
0.719765
 
4.5%
0.715762
 
4.3%
0.722760
 
4.2%
0.717758
 
4.0%
0.721757
 
4.0%
0.720757
 
4.0%
0.718755
 
3.8%
Other values (96)808
56.1%
ValueCountFrequency (%)
0.58971
0.1%
0.59671
0.1%
0.59971
0.1%
0.60371
0.1%
0.60471
0.1%
ValueCountFrequency (%)
0.73572
 
0.1%
0.73473
 
0.2%
0.73372
 
0.1%
0.73351
 
0.1%
0.73279
0.6%

ch3
Real number (ℝ≥0)

Distinct67
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.14666125
Minimum1.1267
Maximum1.1647
Zeros0
Zeros (%)0.0%
Memory size11.4 KiB
2021-04-26T01:44:57.606817image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1.1267
5-th percentile1.1367
Q11.1417
median1.1467
Q31.1515
95-th percentile1.1577
Maximum1.1647
Range0.038
Interquartile range (IQR)0.0098

Descriptive statistics

Standard deviation0.006417434002
Coefficient of variation (CV)0.005596625858
Kurtosis-0.4205498814
Mean1.14666125
Median Absolute Deviation (MAD)0.0049
Skewness0.02141966533
Sum1651.1922
Variance4.118345917 × 105
MonotocityNot monotonic
2021-04-26T01:44:57.776366image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.146781
 
5.6%
1.147778
 
5.4%
1.150778
 
5.4%
1.144775
 
5.2%
1.151774
 
5.1%
1.145774
 
5.1%
1.142772
 
5.0%
1.148765
 
4.5%
1.143764
 
4.4%
1.149761
 
4.2%
Other values (57)718
49.9%
ValueCountFrequency (%)
1.12671
 
0.1%
1.12771
 
0.1%
1.12971
 
0.1%
1.13072
 
0.1%
1.13177
0.5%
ValueCountFrequency (%)
1.16471
 
0.1%
1.16273
0.2%
1.16251
 
0.1%
1.16171
 
0.1%
1.16151
 
0.1%

ch4
Real number (ℝ≥0)

Distinct68
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.221520278
Minimum1.2047
Maximum1.2427
Zeros0
Zeros (%)0.0%
Memory size11.4 KiB
2021-04-26T01:44:57.923005image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1.2047
5-th percentile1.2117
Q11.2157
median1.2205
Q31.2277
95-th percentile1.2345
Maximum1.2427
Range0.038
Interquartile range (IQR)0.012

Descriptive statistics

Standard deviation0.00728035081
Coefficient of variation (CV)0.005960073641
Kurtosis-0.7673517706
Mean1.221520278
Median Absolute Deviation (MAD)0.0058
Skewness0.3333817274
Sum1758.9892
Variance5.300350791 × 105
MonotocityNot monotonic
2021-04-26T01:44:58.079587image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.215787
 
6.0%
1.214775
 
5.2%
1.216774
 
5.1%
1.217765
 
4.5%
1.219764
 
4.4%
1.212763
 
4.4%
1.218762
 
4.3%
1.213761
 
4.2%
1.224756
 
3.9%
1.228755
 
3.8%
Other values (58)778
54.0%
ValueCountFrequency (%)
1.20471
 
0.1%
1.20675
0.3%
1.20773
 
0.2%
1.20852
 
0.1%
1.20878
0.6%
ValueCountFrequency (%)
1.24271
 
0.1%
1.24171
 
0.1%
1.24072
0.1%
1.23973
0.2%
1.23874
0.3%

ch5
Real number (ℝ≥0)

Distinct118
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.066545278
Minimum1.0267
Maximum1.0937
Zeros0
Zeros (%)0.0%
Memory size11.4 KiB
2021-04-26T01:44:58.226194image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1.0267
5-th percentile1.0347
Q11.0557
median1.0717
Q31.0797
95-th percentile1.0857
Maximum1.0937
Range0.067
Interquartile range (IQR)0.024

Descriptive statistics

Standard deviation0.0166731171
Coefficient of variation (CV)0.01563282633
Kurtosis-0.6576875192
Mean1.066545278
Median Absolute Deviation (MAD)0.01
Skewness-0.7275434463
Sum1535.8252
Variance0.0002779928338
MonotocityNot monotonic
2021-04-26T01:44:58.378068image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.079760
 
4.2%
1.076754
 
3.8%
1.081752
 
3.6%
1.075750
 
3.5%
1.074748
 
3.3%
1.078744
 
3.1%
1.083740
 
2.8%
1.077740
 
2.8%
1.082738
 
2.6%
1.084736
 
2.5%
Other values (108)978
67.9%
ValueCountFrequency (%)
1.02673
0.2%
1.02774
0.3%
1.02872
 
0.1%
1.02977
0.5%
1.03075
0.3%
ValueCountFrequency (%)
1.09372
 
0.1%
1.09271
 
0.1%
1.09172
 
0.1%
1.09076
0.4%
1.08979
0.6%

ch6
Real number (ℝ≥0)

Distinct68
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.212456389
Minimum1.1967
Maximum1.2345
Zeros0
Zeros (%)0.0%
Memory size11.4 KiB
2021-04-26T01:44:58.526671image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1.1967
5-th percentile1.2017
Q11.2057
median1.2107
Q31.2187
95-th percentile1.2257
Maximum1.2345
Range0.0378
Interquartile range (IQR)0.013

Descriptive statistics

Standard deviation0.007826910565
Coefficient of variation (CV)0.006455416159
Kurtosis-0.9579024664
Mean1.212456389
Median Absolute Deviation (MAD)0.006
Skewness0.3186357128
Sum1745.9372
Variance6.126052899 × 105
MonotocityNot monotonic
2021-04-26T01:44:58.808611image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.207783
 
5.8%
1.204780
 
5.6%
1.205779
 
5.5%
1.206770
 
4.9%
1.209760
 
4.2%
1.208754
 
3.8%
1.220750
 
3.5%
1.203749
 
3.4%
1.223748
 
3.3%
1.219747
 
3.3%
Other values (58)820
56.9%
ValueCountFrequency (%)
1.19672
 
0.1%
1.19774
 
0.3%
1.19878
0.6%
1.19952
 
0.1%
1.199711
0.8%
ValueCountFrequency (%)
1.23451
 
0.1%
1.23171
 
0.1%
1.23151
 
0.1%
1.23073
0.2%
1.23053
0.2%

infusion_pressure
Real number (ℝ≥0)

ZEROS

Distinct175
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.700560391
Minimum0
Maximum6.49879837
Zeros307
Zeros (%)21.3%
Memory size11.4 KiB
2021-04-26T01:44:58.979178image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.81254518
median6.396168709
Q36.424210548
95-th percentile6.457351685
Maximum6.49879837
Range6.49879837
Interquartile range (IQR)4.611665368

Descriptive statistics

Standard deviation2.754220168
Coefficient of variation (CV)0.5859344287
Kurtosis-0.8518217859
Mean4.700560391
Median Absolute Deviation (MAD)0.03314065933
Skewness-1.042766012
Sum6768.806964
Variance7.585728731
MonotocityNot monotonic
2021-04-26T01:44:59.135725image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0307
 
21.3%
6.38342189842
 
2.9%
6.41911268242
 
2.9%
6.41401386340
 
2.8%
6.39106988938
 
2.6%
6.40126705237
 
2.6%
6.37832355536
 
2.5%
6.42166137735
 
2.4%
6.38087272635
 
2.4%
6.38597154634
 
2.4%
Other values (165)794
55.1%
ValueCountFrequency (%)
0307
21.3%
0.017845030871
 
0.1%
0.025478713212
 
0.1%
0.025492899128
 
0.6%
0.025507086891
 
0.1%
ValueCountFrequency (%)
6.498798373
0.2%
6.4955911641
 
0.1%
6.4930415152
0.1%
6.4879426962
0.1%
6.4853935242
0.1%

infusion_temp
Real number (ℝ≥0)

Distinct10
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.06208333
Minimum25.6
Maximum26.5
Zeros0
Zeros (%)0.0%
Memory size11.4 KiB
2021-04-26T01:44:59.270366image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum25.6
5-th percentile25.8
Q125.9
median26.1
Q326.2
95-th percentile26.3
Maximum26.5
Range0.9
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.1721582534
Coefficient of variation (CV)0.006605698063
Kurtosis-0.5731020597
Mean26.06208333
Median Absolute Deviation (MAD)0.1
Skewness-0.3120564206
Sum37529.4
Variance0.02963846421
MonotocityNot monotonic
2021-04-26T01:44:59.400019image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
26.2373
25.9%
26.1294
20.4%
25.9274
19.0%
26170
11.8%
26.3126
 
8.8%
25.8112
 
7.8%
25.741
 
2.8%
26.436
 
2.5%
25.613
 
0.9%
26.51
 
0.1%
ValueCountFrequency (%)
25.613
 
0.9%
25.741
 
2.8%
25.8112
7.8%
25.9274
19.0%
26170
11.8%
ValueCountFrequency (%)
26.51
 
0.1%
26.436
 
2.5%
26.3126
 
8.8%
26.2373
25.9%
26.1294
20.4%

infusion_vacuum
Real number (ℝ≥0)

Distinct295
Distinct (%)20.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean567.5036811
Minimum0.6564416289
Maximum719.9078369
Zeros0
Zeros (%)0.0%
Memory size11.4 KiB
2021-04-26T01:44:59.542637image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0.6564416289
5-th percentile0.9053501517
Q1712.3053589
median714.7166138
Q3715.9078369
95-th percentile719.510498
Maximum719.9078369
Range719.2513953
Interquartile range (IQR)3.602478027

Descriptive statistics

Standard deviation287.3416676
Coefficient of variation (CV)0.5063256454
Kurtosis0.09788122464
Mean567.5036811
Median Absolute Deviation (MAD)1.191223145
Skewness-1.442782579
Sum817205.3007
Variance82565.23393
MonotocityNot monotonic
2021-04-26T01:44:59.692238image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
714.3200073170
 
11.8%
714.7166138167
 
11.6%
715.9078369115
 
8.0%
715.1134644103
 
7.2%
715.51049892
 
6.4%
713.924804787
 
6.0%
716.70306470
 
4.9%
712.305358969
 
4.8%
719.907836968
 
4.7%
716.305358963
 
4.4%
Other values (285)436
30.3%
ValueCountFrequency (%)
0.65644162891
0.1%
0.65851873161
0.1%
0.66071450711
0.1%
0.66328513621
0.1%
0.66549682621
0.1%
ValueCountFrequency (%)
719.907836968
4.7%
719.51049827
 
1.9%
719.11346448
 
0.6%
718.71661384
 
0.3%
717.89636231
 
0.1%

Interactions

2021-04-26T01:44:46.308556image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T01:44:46.450244image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T01:44:46.579898image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T01:44:46.711049image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T01:44:46.836379image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T01:44:46.962043image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T01:44:47.086714image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T01:44:47.208384image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T01:44:47.350006image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T01:44:47.475670image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T01:44:47.608315image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T01:44:47.797840image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T01:44:47.926467image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T01:44:48.059112image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T01:44:48.188885image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T01:44:48.316065image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T01:44:48.451940image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T01:44:48.595564image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T01:44:48.728207image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T01:44:48.860831image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T01:44:48.993476image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T01:44:49.129180image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T01:44:49.261821image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T01:44:49.392476image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T01:44:49.536010image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T01:44:49.671445image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T01:44:49.802736image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T01:44:49.935381image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T01:44:50.065625image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T01:44:50.205698image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T01:44:50.346797image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T01:44:50.491411image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T01:44:50.626149image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T01:44:50.763685image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T01:44:50.974157image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T01:44:51.113753image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T01:44:51.257369image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T01:44:51.391559image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T01:44:51.527196image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T01:44:51.656369image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T01:44:51.787938image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T01:44:51.962099image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T01:44:52.098401image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T01:44:52.234643image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T01:44:52.367288image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T01:44:52.499933image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T01:44:52.635571image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T01:44:52.766221image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T01:44:52.898867image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T01:44:53.026560image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T01:44:53.168730image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T01:44:53.296927image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T01:44:53.426913image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T01:44:53.563294image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T01:44:53.696115image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T01:44:53.821779image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T01:44:53.953427image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T01:44:54.078094image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T01:44:54.218664image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T01:44:54.362279image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T01:44:54.512877image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T01:44:54.668428image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T01:44:54.907055image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T01:44:55.035711image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T01:44:55.170457image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T01:44:55.303679image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T01:44:55.454277image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T01:44:55.590950image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T01:44:55.721600image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T01:44:55.854246image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T01:44:55.987893image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T01:44:56.117546image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Correlations

2021-04-26T01:44:59.831290image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-04-26T01:45:00.009827image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-04-26T01:45:00.177919image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-04-26T01:45:00.334509image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-04-26T01:44:56.316589image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
A simple visualization of nullity by column.
2021-04-26T01:44:56.478655image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

timech1ch2ch3ch4ch5ch6infusion_pressureinfusion_tempinfusion_vacuum
02021-04-23 16:00:36.3510728001.10770.71871.14371.21071.07771.20076.41146426.0713.924805
12021-04-23 16:01:36.3411609001.10570.71371.14171.21871.07571.20076.41146426.0714.716614
22021-04-23 16:02:36.3639461001.11050.71271.13471.21171.07471.20776.41401426.0714.320007
32021-04-23 16:03:36.3512269001.10970.71551.13871.21271.07471.20276.40891526.0714.320007
42021-04-23 16:04:36.3573670001.10570.71771.13751.21571.06971.20676.40891526.0713.924805
52021-04-23 16:05:36.3467845001.10970.72071.14371.21571.07871.21076.41146426.0713.924805
62021-04-23 16:06:36.3513209001.10970.72271.14371.21171.07771.19976.41146426.0714.320007
72021-04-23 16:07:36.3608777001.10370.71571.14471.21751.06971.20776.40636626.0714.716614
82021-04-23 16:08:36.3478424001.11170.71571.14571.20771.07651.20576.41146426.0713.924805
92021-04-23 16:09:36.3551757001.10470.72371.13671.20971.08071.20356.40891526.0714.320007

Last rows

timech1ch2ch3ch4ch5ch6infusion_pressureinfusion_tempinfusion_vacuum
14302021-04-24 15:50:36.9433822001.10870.71071.14271.21471.06171.20876.41146426.1715.907837
14312021-04-24 15:51:36.9455332001.11070.71371.13371.21371.06071.20576.41401426.1715.510498
14322021-04-24 15:52:36.9527520001.10450.70971.13571.21871.06171.20576.41401426.1719.907837
14332021-04-24 15:53:36.9574883001.10470.71151.14371.21571.05571.20276.41146426.1715.907837
14342021-04-24 15:54:36.9485932001.10670.70871.13851.21271.05571.20676.41146426.1715.510498
14352021-04-24 15:55:36.9365895001.08970.70671.13771.21271.06271.19976.41401426.1715.907837
14362021-04-24 15:56:36.9395911001.10170.70771.13471.21771.05271.20276.40891526.1719.907837
14372021-04-24 15:57:36.9298905001.10570.71471.13471.20851.05871.20976.41401426.1719.510498
14382021-04-24 15:58:36.9374539001.10370.71271.14071.21471.05651.20076.41146426.1715.113464
14392021-04-24 15:59:36.9245598001.10870.71271.13171.21871.05771.20456.41401426.1715.907837